OpenRaiser/Intern-Atlas huggingface dataset:
Compositional Fine-Grained Low-Shot Learning
Venue: arXiv cs.CV 2021
Year: 2021

We develop a novel compositional generative model for zero- and few-shot learning to recognize fine-grained classes with a few or no training samples. Our key observation is that generating holistic features for fine-grained classes fails to capture small attribute differences between classes. Therefore, we propose a feature composition framework that learns to extract attribute features from training samples and combines them to construct fine-grained features for rare and unseen classes. Feature composition allows us to not only selectively compose features of every class from only relevant training samples, but also obtain diversity among composed features via changing samples used for the composition. In addition, instead of building holistic features for classes, we use our attribute features to form dense representations capable of capturing fine-grained attribute details of classes. We propose a training scheme that uses a discriminative model to construct features that are subsequently used to train the model itself. Therefore, we directly train the discriminative model on the composed features without learning a separate generative model. We conduct experiments on four popular datasets of DeepFashion, AWA2, CUB, and SUN, showing the effectiveness of our method.
Elliptical Ordinal Embedding
Venue: arXiv cs.LG 2021
Year: 2021

Ordinal embedding aims at finding a low dimensional representation of objects from a set of constraints of the form "item $j$ is closer to item $i$ than item $k$". Typically, each object is mapped onto a point vector in a low dimensional metric space. We argue that mapping to a density instead of a point vector provides some interesting advantages, including an inherent reflection of the uncertainty about the representation itself and its relative location in the space. Indeed, in this paper, we propose to embed each object as a Gaussian distribution. We investigate the ability of these embeddings to capture the underlying structure of the data while satisfying the constraints, and explore properties of the representation. Experiments on synthetic and real-world datasets showcase the advantages of our approach. In addition, we illustrate the merit of modelling uncertainty, which enriches the visual perception of the mapped objects in the space.
Sparse Layered MIMO with Iterative Detection
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we propose a novel transmission scheme, called sparse layered MIMO (SL-MIMO), that combines non-orthogonal transmission and singular value decomposition (SVD) precoding. Nonorthogonality in SL-MIMO allows re-using of the eigen-channels which improves the spectral efficiency and error rate performance of the system through enhancing the coding gain and diversity gain. We also present a low-complexity message-passing (MP) detector for the proposed SL-MIMO system which performs quite close to maximum likelihood (ML). The joint moment generating function (MGF) of the ordered eigenvalues is calculated and used to derive a closed-form upper bound on the average word error probability (AWEP) of the SL-MIMO system, and this derived expression is then used to analyze the diversity gain of the system. We use our analytical results to design sub-optimal codebooks to minimize the error rate of the SL-MIMO system. Simulation results in 4x4 and 6x6 multiple-input multiple-output (MIMO) systems with 4-ary, 16-ary, and 64-ary constellations show that our proposed SL-MIMO scheme outperforms competing approaches such as X- and Y-codes in terms of system error rate performance. SL-MIMO has 5.6 dB advantage compared to X-codes and 4.7 dB advantage compared to Y-codes in 6x6 MIMO system with a 64-ary constellation.
Conduit: A C++ Library for Best-effort High Performance Computing
Venue: arXiv cs.DC 2021
Year: 2021

Developing software to effectively take advantage of growth in parallel and distributed processing capacity poses significant challenges. Traditional programming techniques allow a user to assume that execution, message passing, and memory are always kept synchronized. However, maintaining this consistency becomes increasingly costly at scale. One proposed strategy is "best-effort computing", which relaxes synchronization and hardware reliability requirements, accepting nondeterminism in exchange for efficiency. Although many programming languages and frameworks aim to facilitate software development for high performance applications, existing tools do not directly provide a prepackaged best-effort interface. The Conduit C++ Library aims to provide such an interface for convenient implementation of software that uses best-effort inter-thread and inter-process communication. Here, we describe the motivation, objectives, design, and implementation of the library. Benchmarks on a communication-intensive graph coloring problem and a compute-intensive digital evolution simulation show that Conduit's best-effort model can improve scaling efficiency and solution quality, particularly in a distributed, multi-node context.
Exploring Ethereum's Data Stores: A Cost and Performance Comparison
Venue: arXiv cs.DB 2021
Year: 2021

The cost of using a blockchain infrastructure as well as the time required to search and retrieve information from it must be considered when designing a decentralized application. In this work, we examine a comprehensive set of data management approaches for Ethereum applications and assess the associated cost in gas as well as the retrieval performance. More precisely, we analyze the storage and retrieval of various-sized data, utilizing smart contract storage. In addition, we study hybrid approaches by using IPFS and Swarm as storage platforms along with Ethereum as a timestamping proof mechanism. Such schemes are especially effective when large chunks of data have to be managed. Moreover, we present methods for low-cost data handling in Ethereum, namely the event-logs, the transaction payload, and the almost surprising exploitation of unused function arguments. Finally, we evaluate these methods on a comprehensive set of experiments.
Semi-Supervised Audio Representation Learning for Modeling Beehive Strengths
Venue: arXiv cs.SD 2021
Year: 2021

Honey bees are critical to our ecosystem and food security as a pollinator, contributing 35% of our global agriculture yield. In spite of their importance, beekeeping is exclusively dependent on human labor and experience-derived heuristics, while requiring frequent human checkups to ensure the colony is healthy, which can disrupt the colony. Increasingly, pollinator populations are declining due to threats from climate change, pests, environmental toxicity, making their management even more critical than ever before in order to ensure sustained global food security. To start addressing this pressing challenge, we developed an integrated hardware sensing system for beehive monitoring through audio and environment measurements, and a hierarchical semi-supervised deep learning model, composed of an audio modeling module and a predictor, to model the strength of beehives. The model is trained jointly on audio reconstruction and prediction losses based on human inspections, in order to model both low-level audio features and circadian temporal dynamics. We show that this model performs well despite limited labels, and can learn an audio embedding that is useful for characterizing different sound profiles of beehives. This is the first instance to our knowledge of applying audio-based deep learning to model beehives and population size in an observational setting across a large number of hives.
FB: A Flexible Buffer Management Scheme for Data Center Switches
Venue: arXiv cs.NI 2021
Year: 2021

Today, network devices share buffer across priority queues to avoid drops during transient congestion. While cost-effective most of the time, this sharing can cause undesired interference among seemingly independent traffic. As a result, low-priority traffic can cause increased packet loss to high-priority traffic. Similarly, long flows can prevent the buffer from absorbing incoming bursts even if they do not share the same queue. The cause of this perhaps unintuitive outcome is that today's buffer sharing techniques are unable to guarantee isolation across (priority) queues without statically allocating buffer space. To address this issue, we designed FB, a novel buffer sharing scheme that offers strict isolation guarantees to high-priority traffic without sacrificing link utilizations. Thus, FB outperforms conventional buffer sharing algorithms in absorbing bursts while achieving on-par throughput. We show that FB is practical and runs at line-rate on existing hardware (Barefoot Tofino). Significantly, FB's operations can be approximated in non-programmable devices.
GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching
Venue: arXiv cs.AR 2021
Year: 2021

Graph neural networks (GNN) analysis engines are vital for real-world problems that use large graph models. Challenges for a GNN hardware platform include the ability to (a) host a variety of GNNs, (b) handle high sparsity in input vertex feature vectors and the graph adjacency matrix and the accompanying random memory access patterns, and (c) maintain load-balanced computation in the face of uneven workloads, induced by high sparsity and power-law vertex degree distributions. This paper proposes GNNIE, an accelerator designed to run a broad range of GNNs. It tackles workload imbalance by (i)~splitting vertex feature operands into blocks, (ii)~reordering and redistributing computations, (iii)~using a novel flexible MAC architecture. It adopts a graph-specific, degree-aware caching policy that is well suited to real-world graph characteristics. The policy enhances on-chip data reuse and avoids random memory access to DRAM. GNNIE achieves average speedups of 21233x over a CPU and 699x over a GPU over multiple datasets on graph attention networks (GATs), graph convolutional networks (GCNs), GraphSAGE, GINConv, and DiffPool. Compared to prior approaches, GNNIE achieves an average speedup of 35x over HyGCN (which cannot implement GATs) for GCN, GraphSAGE, and GINConv, and, using 3.4x fewer processing units, an average speedup of 2.1x over AWB-GCN (which runs only GCNs).
Techniques Toward Optimizing Viewability in RTB Ad Campaigns Using Reinforcement Learning
Venue: arXiv cs.LG 2021
Year: 2021

Reinforcement learning (RL) is an effective technique for training decision-making agents through interactions with their environment. The advent of deep learning has been associated with highly notable successes with sequential decision making problems - such as defeating some of the highest-ranked human players at Go. In digital advertising, real-time bidding (RTB) is a common method of allocating advertising inventory through real-time auctions. Bidding strategies need to incorporate logic for dynamically adjusting parameters in order to deliver pre-assigned campaign goals. Here we discuss techniques toward using RL to train bidding agents. As a campaign metric we particularly focused on viewability: the percentage of inventory which goes on to be viewed by an end user. This paper is presented as a survey of techniques and experiments which we developed through the course of this research. We discuss expanding our training data to include edge cases by training on simulated interactions. We discuss the experimental results comparing the performance of several promising RL algorithms, and an approach to hyperparameter optimization of an actor/critic training pipeline through Bayesian optimization. Finally, we present live-traffic tests of some of our RL agents against a rule-based feedback-control approach, demonstrating the potential for this method as well as areas for further improvement. This paper therefore presents an arrangement of our findings in this quickly developing field, and ways that it can be applied to an RTB use case.
Soft robotic suits: State of the art, core technologies and open challenges
Venue: arXiv cs.RO 2021
Year: 2021

Wearable robots are undergoing a disruptive transition, from the rigid machines that populated the science-fiction world in the early eighties to lightweight robotic apparel, hardly distinguishable from our daily clothes. In less than a decade of development, soft robotic suits have achieved important results in human motor assistance and augmentation. In this paper, we start by giving a definition of soft robotic suits and proposing a taxonomy to classify existing systems. We then critically review the modes of actuation, the physical human-robot interface and the intention-detection strategies of state of the art soft robotic suits, highlighting the advantages and limitations of different approaches. Finally, we discuss the impact of this new technology on human movements, for both augmenting human function and supporting motor impairments, and identify areas that are in need of further development.
Programming and Deployment of Autonomous Swarms using Multi-Agent Reinforcement Learning
Venue: arXiv cs.MA 2021
Year: 2021

Autonomous systems (AS) carry out complex missions by continuously observing the state of their surroundings and taking actions toward a goal. Swarms of AS working together can complete missions faster and more effectively than single AS alone. To build swarms today, developers handcraft their own software for storing, aggregating, and learning from observations. We present the Fleet Computer, a platform for developing and managing swarms. The Fleet Computer provides a programming paradigm that simplifies multi-agent reinforcement learning (MARL) -- an emerging class of algorithms that coordinate swarms of agents. Using just two programmer-provided functions Map() and Eval(), the Fleet Computer compiles and deploys swarms and continuously updates the reinforcement learning models that govern actions. To conserve compute resources, the Fleet Computer gives priority scheduling to models that contribute to effective actions, drawing a novel link between online learning and resource management. We developed swarms for unmanned aerial vehicles (UAV) in agriculture and for video analytics on urban traffic. Compared to individual AS, our swarms achieved speedup of 4.4X using 4 UAV and 62X using 130 video cameras. Compared to a competing approach for building swarms that is widely used in practice, our swarms were 3X more effective, using 3.9X less energy.
Runtime Enforcement of Programmable Logic Controllers
Venue: arXiv cs.CR 2021
Year: 2021

With the advent of Industry 4.0, industrial facilities and critical infrastructures are transforming into an ecosystem of heterogeneous physical and cyber components, such as programmable logic controllers, increasingly interconnected and therefore exposed to cyber-physical attacks, i.e., security breaches in cyberspace that may adversely affect the physical processes underlying industrial control systems. In this paper, we propose a formal approach based on runtime enforcement to ensure specification compliance in networks of controllers, possibly compromised by colluding malware that may tamper with actuator commands, sensor readings, and inter-controller communications. Our approach relies on an ad-hoc sub-class of Ligatti et al.'s edit automata to enforce controllers represented in Hennessy and Regan's Timed Process Language. We define a synthesis algorithm that, given an alphabet $P$ of observable actions and a timed correctness property $e$, returns a monitor that enforces the property $e$ during the execution of any (potentially corrupted) controller with alphabet $P$, and complying with the property $e$. Our monitors correct and suppress incorrect actions coming from corrupted controllers and emit actions in full autonomy when the controller under scrutiny is not able to do so in a correct manner. Besides classical requirements, such as transparency and soundness, the proposed enforcement enjoys deadlock- and diverge-freedom of monitored controllers, together with scalability when dealing with networks of controllers. Finally, we test the proposed enforcement mechanism on a non-trivial case study, taken from the context of industrial water treatment systems, in which the controllers are injected with different malware with different malicious goals.
Cybercosm: New Foundations for a Converged Science Data Ecosystem
Venue: arXiv cs.NI 2021
Year: 2021

Scientific communities naturally tend to organize around data ecosystems created by the combination of their observational devices, their data repositories, and the workflows essential to carry their research from observation to discovery. However, these legacy data ecosystems are now breaking down under the pressure of the exponential growth in the volume and velocity of these workflows, which are further complicated by the need to integrate the highly data intensive methods of the Artificial Intelligence revolution. Enabling ground breaking science that makes full use of this new, data saturated research environment will require distributed systems that support dramatically improved resource sharing, workflow portability and composability, and data ecosystem convergence. The Cybercosm vision presented in this white paper describes a radically different approach to the architecture of distributed systems for data-intensive science and its application workflows. As opposed to traditional models that restrict interoperability by hiving off storage, networking, and computing resources in separate technology silos, Cybercosm defines a minimally sufficient hypervisor as a spanning layer for its data plane that virtualizes and converges the local resources of the system's nodes in a fully interoperable manner. By building on a common, universal interface into which the problems that infect today's data-intensive workflows can be decomposed and attacked, Cybercosm aims to support scalable, portable and composable workflows that span and merge the distributed data ecosystems that characterize leading edge research communities today.
Soccer Player Tracking in Low Quality Video
Venue: arXiv cs.CV 2021
Year: 2021

In this paper we propose a system capable of tracking multiple soccer players in different types of video quality. The main goal, in contrast to most state-of-art soccer player tracking systems, is the ability of execute effectively tracking in videos of low-quality. We adapted a state-of-art Multiple Object Tracking to the task. In order to do that adaptation, we created a Detection and a Tracking Dataset for 3 different qualities of video. The results of our system are conclusive of its high performance.
SPATO: A Student Project Allocation Based Task Offloading in IoT-Fog Systems
Venue: arXiv cs.NI 2021
Year: 2021

The Internet of Things (IoT) devices are highly reliant on cloud systems to meet their storage and computational demands. However, due to the remote location of cloud servers, IoT devices often suffer from intermittent Wide Area Network (WAN) latency which makes execution of delay-critical IoT applications inconceivable. To overcome this, service providers (SPs) often deploy multiple fog nodes (FNs) at the network edge that helps in executing offloaded computations from IoT devices with improved user experience. As the FNs have limited resources, matching IoT services to FNs while ensuring minimum latency and energy from an end-user's perspective and maximizing revenue and tasks meeting deadlines from an SP's standpoint is challenging. Therefore in this paper, we propose a student project allocation (SPA) based efficient task offloading strategy called SPATO that takes into account key parameters from different stakeholders. Thorough simulation analysis shows that SPATO is able to reduce the offloading energy and latency respectively by 29% and 40% and improves the revenue by 25% with 99.3% of tasks executing within their deadline.
Machine Learning Regression based Single Event Transient Modeling Method for Circuit-Level Simulation
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, a novel machine learning regression based single event transient (SET) modeling method is proposed. The proposed method can obtain a reasonable and accurate model without considering the complex physical mechanism. We got plenty of SET current data of SMIC 130nm bulk CMOS by TCAD simulation under different conditions (e.g. different LET and different drain bias voltage). A multilayer feedfordward neural network is used to build the SET pulse current model by learning the data from TCAD simulation. The proposed model is validated with the simulation results from TCAD simulation. The trained SET pulse current model is implemented as a Verilog-A current source in the Cadence Spectre circuit simulator and an inverter with five fan-outs is used to show the practicability and reasonableness of the proposed SET pulse current model for circuit-level single-event effect (SEE) simulation.
Automatic task-based parallelization of C++ applications by source-to-source transformations
Venue: arXiv cs.DC 2021
Year: 2021

Currently, multi/many-core CPUs are considered standard in most types of computers including, mobile phones, PCs or supercomputers. However, the parallelization of applications as well as refactoring/design of applications for efficient hardware usage remains restricted to experts who have advanced technical knowledge and who can invest time tuning their software. In this context, the compilation community has proposed different methods for automatic parallelization, but their focus is traditionally on loops and nested loops with the support of polyhedral techniques. In this study, we propose a new approach to transform sequential C++ source code into a task-based parallel one by inserting annotations. We explain the different mechanisms we used to create tasks at each function/method call, and how we can limit the number of tasks. Our method can be implemented on top of the OpenMP 4.0 standard. It is compiler-independent and can rely on external well-optimized OpenMP libraries. Finally, we provide preliminary performance results that illustrate the potential of our method.
Q-learning algorithm for resource allocation in WDMA-based optical wireless communication networks
Venue: arXiv cs.NI 2021
Year: 2021

Visible Light Communication (VLC) has been widely investigated during the last decade due to its ability to provide high data rates with low power consumption. In general, resource management is an important issue in cellular networks that can highly effect their performance. In this paper, an optimisation problem is formulated to assign each user to an optimal access point and a wavelength at a given time. This problem can be solved using mixed integer linear programming (MILP). However, using MILP is not considered a practical solution due to its complexity and memory requirements. In addition, accurate information must be provided to perform the resource allocation. Therefore, the optimisation problem is reformulated using reinforcement learning (RL), which has recently received tremendous interest due to its ability to interact with any environment without prior knowledge. In this paper, we investigate solving the resource allocation optimisation problem in VLC systems using the basic Q-learning algorithm. Two scenarios are simulated to compare the results with the previously proposed MILP model. The results demonstrate the ability of the Q-learning algorithm to provide optimal solutions close to the MILP model without prior knowledge of the system.